Overview

Dataset statistics

Number of variables28
Number of observations150000
Missing cells140124
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.0 MiB
Average record size in memory224.0 B

Variable types

Categorical19
Numeric8
Unsupported1

Alerts

ID has a high cardinality: 150000 distinct valuesHigh cardinality
Customer_ID has a high cardinality: 12500 distinct valuesHigh cardinality
Name has a high cardinality: 10139 distinct valuesHigh cardinality
Age has a high cardinality: 2524 distinct valuesHigh cardinality
SSN has a high cardinality: 12501 distinct valuesHigh cardinality
Annual_Income has a high cardinality: 21192 distinct valuesHigh cardinality
Num_of_Loan has a high cardinality: 623 distinct valuesHigh cardinality
Type_of_Loan has a high cardinality: 6260 distinct valuesHigh cardinality
Num_of_Delayed_Payment has a high cardinality: 1058 distinct valuesHigh cardinality
Changed_Credit_Limit has a high cardinality: 4605 distinct valuesHigh cardinality
Outstanding_Debt has a high cardinality: 13622 distinct valuesHigh cardinality
Credit_History_Age has a high cardinality: 408 distinct valuesHigh cardinality
Amount_invested_monthly has a high cardinality: 136497 distinct valuesHigh cardinality
Num_Bank_Accounts is highly overall correlated with Interest_Rate and 1 other fieldsHigh correlation
Interest_Rate is highly overall correlated with Num_Bank_Accounts and 2 other fieldsHigh correlation
Delay_from_due_date is highly overall correlated with Num_Bank_Accounts and 1 other fieldsHigh correlation
Num_Credit_Inquiries is highly overall correlated with Interest_RateHigh correlation
Num_of_Loan is highly imbalanced (60.9%)Imbalance
Num_of_Delayed_Payment is highly imbalanced (50.9%)Imbalance
Name has 15000 (10.0%) missing valuesMissing
Monthly_Inhand_Salary has 22500 (15.0%) missing valuesMissing
Type_of_Loan has 17112 (11.4%) missing valuesMissing
Num_of_Delayed_Payment has 10500 (7.0%) missing valuesMissing
Num_Credit_Inquiries has 3000 (2.0%) missing valuesMissing
Credit_History_Age has 13500 (9.0%) missing valuesMissing
Amount_invested_monthly has 6750 (4.5%) missing valuesMissing
Monthly_Balance has 1762 (1.2%) missing valuesMissing
Credit_Score has 50000 (33.3%) missing valuesMissing
ID is uniformly distributedUniform
Customer_ID is uniformly distributedUniform
Month is uniformly distributedUniform
ID has unique valuesUnique
Credit_Utilization_Ratio has unique valuesUnique
Monthly_Balance is an unsupported type, check if it needs cleaning or further analysisUnsupported
Num_Bank_Accounts has 6494 (4.3%) zerosZeros
Delay_from_due_date has 1821 (1.2%) zerosZeros
Num_Credit_Inquiries has 8074 (5.4%) zerosZeros
Total_EMI_per_month has 15615 (10.4%) zerosZeros

Reproduction

Analysis started2023-04-06 12:09:22.845305
Analysis finished2023-04-06 12:09:57.509888
Duration34.66 seconds
Software versionydata-profiling vv4.1.1
Download configurationconfig.json

Variables

ID
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct150000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0x160a
 
1
0x13b0a
 
1
0x13af2
 
1
0x13af3
 
1
0x13af4
 
1
Other values (149995)
149995 

Length

Max length7
Median length7
Mean length6.6006533
Min length6

Characters and Unicode

Total characters990098
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique150000 ?
Unique (%)100.0%

Sample

1st row0x160a
2nd row0x160b
3rd row0x160c
4th row0x160d
5th row0x1616

Common Values

ValueCountFrequency (%)
0x160a 1
 
< 0.1%
0x13b0a 1
 
< 0.1%
0x13af2 1
 
< 0.1%
0x13af3 1
 
< 0.1%
0x13af4 1
 
< 0.1%
0x13af5 1
 
< 0.1%
0x13afa 1
 
< 0.1%
0x13afb 1
 
< 0.1%
0x13afc 1
 
< 0.1%
0x13afd 1
 
< 0.1%
Other values (149990) 149990
> 99.9%

Length

2023-04-06T09:09:57.669845image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0x160a 1
 
< 0.1%
0x1631 1
 
< 0.1%
0x163c 1
 
< 0.1%
0x163b 1
 
< 0.1%
0x1622 1
 
< 0.1%
0x160c 1
 
< 0.1%
0x160d 1
 
< 0.1%
0x1616 1
 
< 0.1%
0x1617 1
 
< 0.1%
0x1618 1
 
< 0.1%
Other values (149990) 149990
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0 186157
18.8%
x 150000
15.2%
1 104253
10.5%
2 64817
 
6.5%
3 40255
 
4.1%
4 40255
 
4.1%
5 40241
 
4.1%
a 36415
 
3.7%
b 36415
 
3.7%
c 36415
 
3.7%
Other values (7) 254875
25.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 621636
62.8%
Lowercase Letter 368462
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 186157
29.9%
1 104253
16.8%
2 64817
 
10.4%
3 40255
 
6.5%
4 40255
 
6.5%
5 40241
 
6.5%
7 36415
 
5.9%
8 36415
 
5.9%
9 36415
 
5.9%
6 36413
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
x 150000
40.7%
a 36415
 
9.9%
b 36415
 
9.9%
c 36415
 
9.9%
d 36415
 
9.9%
e 36415
 
9.9%
f 36387
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Common 621636
62.8%
Latin 368462
37.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 186157
29.9%
1 104253
16.8%
2 64817
 
10.4%
3 40255
 
6.5%
4 40255
 
6.5%
5 40241
 
6.5%
7 36415
 
5.9%
8 36415
 
5.9%
9 36415
 
5.9%
6 36413
 
5.9%
Latin
ValueCountFrequency (%)
x 150000
40.7%
a 36415
 
9.9%
b 36415
 
9.9%
c 36415
 
9.9%
d 36415
 
9.9%
e 36415
 
9.9%
f 36387
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 990098
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 186157
18.8%
x 150000
15.2%
1 104253
10.5%
2 64817
 
6.5%
3 40255
 
4.1%
4 40255
 
4.1%
5 40241
 
4.1%
a 36415
 
3.7%
b 36415
 
3.7%
c 36415
 
3.7%
Other values (7) 254875
25.7%

Customer_ID
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct12500
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
CUS_0xd40
 
12
CUS_0x9bf4
 
12
CUS_0x5ae3
 
12
CUS_0xbe9a
 
12
CUS_0x4874
 
12
Other values (12495)
149940 

Length

Max length10
Median length10
Mean length9.93952
Min length9

Characters and Unicode

Total characters1490928
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCUS_0xd40
2nd rowCUS_0xd40
3rd rowCUS_0xd40
4th rowCUS_0xd40
5th rowCUS_0x21b1

Common Values

ValueCountFrequency (%)
CUS_0xd40 12
 
< 0.1%
CUS_0x9bf4 12
 
< 0.1%
CUS_0x5ae3 12
 
< 0.1%
CUS_0xbe9a 12
 
< 0.1%
CUS_0x4874 12
 
< 0.1%
CUS_0xc67b 12
 
< 0.1%
CUS_0x8a64 12
 
< 0.1%
CUS_0x35ea 12
 
< 0.1%
CUS_0x5044 12
 
< 0.1%
CUS_0x9dfd 12
 
< 0.1%
Other values (12490) 149880
99.9%

Length

2023-04-06T09:09:57.829844image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cus_0xd40 12
 
< 0.1%
cus_0x75c6 12
 
< 0.1%
cus_0x5b48 12
 
< 0.1%
cus_0xc0ab 12
 
< 0.1%
cus_0x2dbc 12
 
< 0.1%
cus_0xb891 12
 
< 0.1%
cus_0x1cdb 12
 
< 0.1%
cus_0x95ee 12
 
< 0.1%
cus_0x284a 12
 
< 0.1%
cus_0x5407 12
 
< 0.1%
Other values (12490) 149880
99.9%

Most occurring characters

ValueCountFrequency (%)
0 177372
11.9%
C 150000
 
10.1%
S 150000
 
10.1%
_ 150000
 
10.1%
x 150000
 
10.1%
U 150000
 
10.1%
4 42000
 
2.8%
6 41100
 
2.8%
5 40800
 
2.7%
3 40608
 
2.7%
Other values (11) 399048
26.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 541812
36.3%
Uppercase Letter 450000
30.2%
Lowercase Letter 349116
23.4%
Connector Punctuation 150000
 
10.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 177372
32.7%
4 42000
 
7.8%
6 41100
 
7.6%
5 40800
 
7.5%
3 40608
 
7.5%
8 40608
 
7.5%
7 40164
 
7.4%
9 40104
 
7.4%
2 40080
 
7.4%
1 38976
 
7.2%
Lowercase Letter
ValueCountFrequency (%)
x 150000
43.0%
b 40200
 
11.5%
a 39816
 
11.4%
c 33408
 
9.6%
e 29232
 
8.4%
d 28308
 
8.1%
f 28152
 
8.1%
Uppercase Letter
ValueCountFrequency (%)
C 150000
33.3%
S 150000
33.3%
U 150000
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 150000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 799116
53.6%
Common 691812
46.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 177372
25.6%
_ 150000
21.7%
4 42000
 
6.1%
6 41100
 
5.9%
5 40800
 
5.9%
3 40608
 
5.9%
8 40608
 
5.9%
7 40164
 
5.8%
9 40104
 
5.8%
2 40080
 
5.8%
Latin
ValueCountFrequency (%)
C 150000
18.8%
S 150000
18.8%
x 150000
18.8%
U 150000
18.8%
b 40200
 
5.0%
a 39816
 
5.0%
c 33408
 
4.2%
e 29232
 
3.7%
d 28308
 
3.5%
f 28152
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1490928
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 177372
11.9%
C 150000
 
10.1%
S 150000
 
10.1%
_ 150000
 
10.1%
x 150000
 
10.1%
U 150000
 
10.1%
4 42000
 
2.8%
6 41100
 
2.8%
5 40800
 
2.7%
3 40608
 
2.7%
Other values (11) 399048
26.8%

Month
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
September
12500 
October
12500 
November
12500 
December
12500 
January
12500 
Other values (7)
87500 

Length

Max length9
Median length7
Mean length6.1666667
Min length3

Characters and Unicode

Total characters925000
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSeptember
2nd rowOctober
3rd rowNovember
4th rowDecember
5th rowSeptember

Common Values

ValueCountFrequency (%)
September 12500
8.3%
October 12500
8.3%
November 12500
8.3%
December 12500
8.3%
January 12500
8.3%
February 12500
8.3%
March 12500
8.3%
April 12500
8.3%
May 12500
8.3%
June 12500
8.3%
Other values (2) 25000
16.7%

Length

2023-04-06T09:09:58.000385image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
september 12500
8.3%
october 12500
8.3%
november 12500
8.3%
december 12500
8.3%
january 12500
8.3%
february 12500
8.3%
march 12500
8.3%
april 12500
8.3%
may 12500
8.3%
june 12500
8.3%
Other values (2) 25000
16.7%

Most occurring characters

ValueCountFrequency (%)
e 137500
14.9%
r 112500
12.2%
u 75000
 
8.1%
b 62500
 
6.8%
a 62500
 
6.8%
y 50000
 
5.4%
J 37500
 
4.1%
t 37500
 
4.1%
m 37500
 
4.1%
c 37500
 
4.1%
Other values (16) 275000
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 775000
83.8%
Uppercase Letter 150000
 
16.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 137500
17.7%
r 112500
14.5%
u 75000
9.7%
b 62500
8.1%
a 62500
8.1%
y 50000
 
6.5%
t 37500
 
4.8%
m 37500
 
4.8%
c 37500
 
4.8%
n 25000
 
3.2%
Other values (8) 137500
17.7%
Uppercase Letter
ValueCountFrequency (%)
J 37500
25.0%
A 25000
16.7%
M 25000
16.7%
S 12500
 
8.3%
F 12500
 
8.3%
D 12500
 
8.3%
N 12500
 
8.3%
O 12500
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 925000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 137500
14.9%
r 112500
12.2%
u 75000
 
8.1%
b 62500
 
6.8%
a 62500
 
6.8%
y 50000
 
5.4%
J 37500
 
4.1%
t 37500
 
4.1%
m 37500
 
4.1%
c 37500
 
4.1%
Other values (16) 275000
29.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 925000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 137500
14.9%
r 112500
12.2%
u 75000
 
8.1%
b 62500
 
6.8%
a 62500
 
6.8%
y 50000
 
5.4%
J 37500
 
4.1%
t 37500
 
4.1%
m 37500
 
4.1%
c 37500
 
4.1%
Other values (16) 275000
29.7%

Name
Categorical

HIGH CARDINALITY  MISSING 

Distinct10139
Distinct (%)7.5%
Missing15000
Missing (%)10.0%
Memory size1.1 MiB
Stevex
 
66
Langep
 
65
Jessicad
 
59
Vaughanl
 
58
Raymondr
 
58
Other values (10134)
134694 

Length

Max length25
Median length20
Mean length9.7659926
Min length2

Characters and Unicode

Total characters1318409
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAaron Maashoh
2nd rowAaron Maashoh
3rd rowAaron Maashoh
4th rowAaron Maashoh
5th rowRick Rothackerj

Common Values

ValueCountFrequency (%)
Stevex 66
 
< 0.1%
Langep 65
 
< 0.1%
Jessicad 59
 
< 0.1%
Vaughanl 58
 
< 0.1%
Raymondr 58
 
< 0.1%
Deepa Seetharamanm 58
 
< 0.1%
Nicko 57
 
< 0.1%
Jessica Wohlt 57
 
< 0.1%
Ronald Groverk 56
 
< 0.1%
Danielz 55
 
< 0.1%
Other values (10129) 134411
89.6%
(Missing) 15000
 
10.0%

Length

2023-04-06T09:09:58.179027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
david 968
 
0.5%
jonathan 913
 
0.5%
jessica 761
 
0.4%
sarah 617
 
0.3%
karen 568
 
0.3%
nick 561
 
0.3%
tim 555
 
0.3%
caroline 553
 
0.3%
john 511
 
0.3%
tom 508
 
0.3%
Other values (9720) 181957
96.5%

Most occurring characters

ValueCountFrequency (%)
a 137614
 
10.4%
e 114139
 
8.7%
n 88845
 
6.7%
i 87745
 
6.7%
r 81658
 
6.2%
o 66930
 
5.1%
l 63199
 
4.8%
53523
 
4.1%
t 52504
 
4.0%
h 45818
 
3.5%
Other values (47) 526434
39.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1071504
81.3%
Uppercase Letter 187988
 
14.3%
Space Separator 53523
 
4.1%
Other Punctuation 3222
 
0.2%
Dash Punctuation 2172
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 137614
12.8%
e 114139
 
10.7%
n 88845
 
8.3%
i 87745
 
8.2%
r 81658
 
7.6%
o 66930
 
6.2%
l 63199
 
5.9%
t 52504
 
4.9%
h 45818
 
4.3%
s 45628
 
4.3%
Other values (16) 287424
26.8%
Uppercase Letter
ValueCountFrequency (%)
S 21401
 
11.4%
A 13103
 
7.0%
M 12985
 
6.9%
L 12584
 
6.7%
J 11986
 
6.4%
C 11670
 
6.2%
R 10719
 
5.7%
D 10512
 
5.6%
K 10341
 
5.5%
B 9782
 
5.2%
Other values (16) 62905
33.5%
Other Punctuation
ValueCountFrequency (%)
. 1649
51.2%
" 1444
44.8%
, 129
 
4.0%
Space Separator
ValueCountFrequency (%)
53523
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1259492
95.5%
Common 58917
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 137614
 
10.9%
e 114139
 
9.1%
n 88845
 
7.1%
i 87745
 
7.0%
r 81658
 
6.5%
o 66930
 
5.3%
l 63199
 
5.0%
t 52504
 
4.2%
h 45818
 
3.6%
s 45628
 
3.6%
Other values (42) 475412
37.7%
Common
ValueCountFrequency (%)
53523
90.8%
- 2172
 
3.7%
. 1649
 
2.8%
" 1444
 
2.5%
, 129
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1318409
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 137614
 
10.4%
e 114139
 
8.7%
n 88845
 
6.7%
i 87745
 
6.7%
r 81658
 
6.2%
o 66930
 
5.1%
l 63199
 
4.8%
53523
 
4.1%
t 52504
 
4.0%
h 45818
 
3.5%
Other values (47) 526434
39.9%

Age
Categorical

Distinct2524
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
39
 
4198
32
 
4189
28
 
4173
26
 
4140
35
 
4130
Other values (2519)
129170 

Length

Max length5
Median length2
Mean length2.1030733
Min length2

Characters and Unicode

Total characters315461
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2084 ?
Unique (%)1.4%

Sample

1st row23
2nd row24
3rd row24
4th row24_
5th row28

Common Values

ValueCountFrequency (%)
39 4198
 
2.8%
32 4189
 
2.8%
28 4173
 
2.8%
26 4140
 
2.8%
35 4130
 
2.8%
44 4116
 
2.7%
38 4099
 
2.7%
27 4089
 
2.7%
31 4071
 
2.7%
22 4063
 
2.7%
Other values (2514) 108732
72.5%

Length

2023-04-06T09:09:58.356402image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
39 4416
 
2.9%
32 4413
 
2.9%
28 4383
 
2.9%
26 4366
 
2.9%
35 4349
 
2.9%
38 4334
 
2.9%
44 4324
 
2.9%
27 4316
 
2.9%
31 4287
 
2.9%
22 4278
 
2.9%
Other values (2434) 106534
71.0%

Most occurring characters

ValueCountFrequency (%)
2 57829
18.3%
3 57493
18.2%
4 50047
15.9%
5 32300
10.2%
1 31218
9.9%
0 17664
 
5.6%
6 15759
 
5.0%
9 15624
 
5.0%
8 14972
 
4.7%
7 13789
 
4.4%
Other values (2) 8766
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 306695
97.2%
Connector Punctuation 7416
 
2.4%
Dash Punctuation 1350
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 57829
18.9%
3 57493
18.7%
4 50047
16.3%
5 32300
10.5%
1 31218
10.2%
0 17664
 
5.8%
6 15759
 
5.1%
9 15624
 
5.1%
8 14972
 
4.9%
7 13789
 
4.5%
Connector Punctuation
ValueCountFrequency (%)
_ 7416
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 315461
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 57829
18.3%
3 57493
18.2%
4 50047
15.9%
5 32300
10.2%
1 31218
9.9%
0 17664
 
5.6%
6 15759
 
5.0%
9 15624
 
5.0%
8 14972
 
4.7%
7 13789
 
4.4%
Other values (2) 8766
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 315461
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 57829
18.3%
3 57493
18.2%
4 50047
15.9%
5 32300
10.2%
1 31218
9.9%
0 17664
 
5.6%
6 15759
 
5.0%
9 15624
 
5.0%
8 14972
 
4.7%
7 13789
 
4.4%
Other values (2) 8766
 
2.8%

SSN
Categorical

Distinct12501
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
#F%$D@*&8
 
8400
078-73-5990
 
12
374-03-0670
 
12
255-39-8777
 
12
866-11-3352
 
12
Other values (12496)
141552 

Length

Max length11
Median length11
Mean length10.888
Min length9

Characters and Unicode

Total characters1633200
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row821-00-0265
2nd row821-00-0265
3rd row821-00-0265
4th row821-00-0265
5th row004-07-5839

Common Values

ValueCountFrequency (%)
#F%$D@*&8 8400
 
5.6%
078-73-5990 12
 
< 0.1%
374-03-0670 12
 
< 0.1%
255-39-8777 12
 
< 0.1%
866-11-3352 12
 
< 0.1%
159-51-7992 12
 
< 0.1%
318-30-9160 12
 
< 0.1%
259-11-0934 12
 
< 0.1%
162-17-7776 12
 
< 0.1%
557-07-3973 12
 
< 0.1%
Other values (12491) 141492
94.3%

Length

2023-04-06T09:09:58.534366image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
f%$d@*&8 8400
 
5.6%
741-04-8469 12
 
< 0.1%
213-49-2021 12
 
< 0.1%
996-52-9835 12
 
< 0.1%
978-19-7269 12
 
< 0.1%
056-57-6013 12
 
< 0.1%
302-82-0750 12
 
< 0.1%
174-73-2790 12
 
< 0.1%
159-72-2454 12
 
< 0.1%
820-41-6857 12
 
< 0.1%
Other values (12491) 141492
94.3%

Most occurring characters

ValueCountFrequency (%)
- 283200
17.3%
8 137348
8.4%
1 129790
7.9%
4 128788
7.9%
2 127869
7.8%
7 127708
7.8%
0 127016
7.8%
9 126945
7.8%
5 126837
7.8%
3 125445
7.7%
Other values (9) 192254
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1282800
78.5%
Dash Punctuation 283200
 
17.3%
Other Punctuation 42000
 
2.6%
Uppercase Letter 16800
 
1.0%
Currency Symbol 8400
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 137348
10.7%
1 129790
10.1%
4 128788
10.0%
2 127869
10.0%
7 127708
10.0%
0 127016
9.9%
9 126945
9.9%
5 126837
9.9%
3 125445
9.8%
6 125054
9.7%
Other Punctuation
ValueCountFrequency (%)
& 8400
20.0%
* 8400
20.0%
@ 8400
20.0%
% 8400
20.0%
# 8400
20.0%
Uppercase Letter
ValueCountFrequency (%)
F 8400
50.0%
D 8400
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 283200
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 8400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1616400
99.0%
Latin 16800
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 283200
17.5%
8 137348
8.5%
1 129790
8.0%
4 128788
8.0%
2 127869
7.9%
7 127708
7.9%
0 127016
7.9%
9 126945
7.9%
5 126837
7.8%
3 125445
7.8%
Other values (7) 175454
10.9%
Latin
ValueCountFrequency (%)
F 8400
50.0%
D 8400
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1633200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 283200
17.3%
8 137348
8.4%
1 129790
7.9%
4 128788
7.9%
2 127869
7.8%
7 127708
7.8%
0 127016
7.8%
9 126945
7.8%
5 126837
7.8%
3 125445
7.7%
Other values (9) 192254
11.8%

Occupation
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
_______
10500 
Lawyer
 
9899
Engineer
 
9562
Architect
 
9550
Mechanic
 
9459
Other values (11)
101030 

Length

Max length13
Median length10
Mean length8.43234
Min length6

Characters and Unicode

Total characters1264851
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowScientist
2nd rowScientist
3rd rowScientist
4th rowScientist
5th row_______

Common Values

ValueCountFrequency (%)
_______ 10500
 
7.0%
Lawyer 9899
 
6.6%
Engineer 9562
 
6.4%
Architect 9550
 
6.4%
Mechanic 9459
 
6.3%
Accountant 9404
 
6.3%
Scientist 9403
 
6.3%
Developer 9381
 
6.3%
Media_Manager 9362
 
6.2%
Teacher 9318
 
6.2%
Other values (6) 54162
36.1%

Length

2023-04-06T09:09:58.718253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10500
 
7.0%
lawyer 9899
 
6.6%
engineer 9562
 
6.4%
architect 9550
 
6.4%
mechanic 9459
 
6.3%
accountant 9404
 
6.3%
scientist 9403
 
6.3%
developer 9381
 
6.3%
media_manager 9362
 
6.2%
teacher 9318
 
6.2%
Other values (6) 54162
36.1%

Most occurring characters

ValueCountFrequency (%)
e 168560
13.3%
r 129748
10.3%
n 111663
 
8.8%
a 102092
 
8.1%
c 93519
 
7.4%
t 93045
 
7.4%
i 92395
 
7.3%
_ 82862
 
6.6%
o 46135
 
3.6%
M 46014
 
3.6%
Other values (18) 298818
23.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1033127
81.7%
Uppercase Letter 148862
 
11.8%
Connector Punctuation 82862
 
6.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 168560
16.3%
r 129748
12.6%
n 111663
10.8%
a 102092
9.9%
c 93519
9.1%
t 93045
9.0%
i 92395
8.9%
o 46135
 
4.5%
u 36661
 
3.5%
h 28327
 
2.7%
Other values (8) 130982
12.7%
Uppercase Letter
ValueCountFrequency (%)
M 46014
30.9%
A 18954
12.7%
E 18839
12.7%
D 18495
12.4%
L 9899
 
6.6%
S 9403
 
6.3%
T 9318
 
6.3%
J 9122
 
6.1%
W 8818
 
5.9%
Connector Punctuation
ValueCountFrequency (%)
_ 82862
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1181989
93.4%
Common 82862
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 168560
14.3%
r 129748
11.0%
n 111663
9.4%
a 102092
 
8.6%
c 93519
 
7.9%
t 93045
 
7.9%
i 92395
 
7.8%
o 46135
 
3.9%
M 46014
 
3.9%
u 36661
 
3.1%
Other values (17) 262157
22.2%
Common
ValueCountFrequency (%)
_ 82862
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1264851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 168560
13.3%
r 129748
10.3%
n 111663
 
8.8%
a 102092
 
8.1%
c 93519
 
7.4%
t 93045
 
7.4%
i 92395
 
7.3%
_ 82862
 
6.6%
o 46135
 
3.6%
M 46014
 
3.6%
Other values (18) 298818
23.6%

Annual_Income
Categorical

Distinct21192
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
36585.12
 
24
9141.63
 
23
95596.35
 
23
20867.67
 
23
17816.75
 
23
Other values (21187)
149884 

Length

Max length19
Median length8
Mean length8.3092267
Min length6

Characters and Unicode

Total characters1246384
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6190 ?
Unique (%)4.1%

Sample

1st row19114.12
2nd row19114.12
3rd row19114.12
4th row19114.12
5th row34847.84

Common Values

ValueCountFrequency (%)
36585.12 24
 
< 0.1%
9141.63 23
 
< 0.1%
95596.35 23
 
< 0.1%
20867.67 23
 
< 0.1%
17816.75 23
 
< 0.1%
33029.66 22
 
< 0.1%
72524.2 22
 
< 0.1%
109945.32 22
 
< 0.1%
17273.83 22
 
< 0.1%
22434.16 21
 
< 0.1%
Other values (21182) 149775
99.9%

Length

2023-04-06T09:09:58.904083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
36585.12 24
 
< 0.1%
109945.32 24
 
< 0.1%
9141.63 24
 
< 0.1%
32543.38 24
 
< 0.1%
22434.16 24
 
< 0.1%
17273.83 24
 
< 0.1%
40341.16 24
 
< 0.1%
17816.75 24
 
< 0.1%
20867.67 24
 
< 0.1%
72524.2 23
 
< 0.1%
Other values (13978) 149761
99.8%

Most occurring characters

ValueCountFrequency (%)
. 150000
12.0%
1 139224
11.2%
2 114510
9.2%
4 107657
8.6%
3 107443
8.6%
8 106304
8.5%
5 106050
8.5%
6 105985
8.5%
9 103560
8.3%
0 99513
8.0%
Other values (2) 106138
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1085884
87.1%
Other Punctuation 150000
 
12.0%
Connector Punctuation 10500
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 139224
12.8%
2 114510
10.5%
4 107657
9.9%
3 107443
9.9%
8 106304
9.8%
5 106050
9.8%
6 105985
9.8%
9 103560
9.5%
0 99513
9.2%
7 95638
8.8%
Other Punctuation
ValueCountFrequency (%)
. 150000
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1246384
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 150000
12.0%
1 139224
11.2%
2 114510
9.2%
4 107657
8.6%
3 107443
8.6%
8 106304
8.5%
5 106050
8.5%
6 105985
8.5%
9 103560
8.3%
0 99513
8.0%
Other values (2) 106138
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1246384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 150000
12.0%
1 139224
11.2%
2 114510
9.2%
4 107657
8.6%
3 107443
8.6%
8 106304
8.5%
5 106050
8.5%
6 105985
8.5%
9 103560
8.3%
0 99513
8.0%
Other values (2) 106138
8.5%

Monthly_Inhand_Salary
Real number (ℝ)

Distinct13683
Distinct (%)10.7%
Missing22500
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean4190.1151
Minimum303.64542
Maximum15204.633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-06T09:09:59.068720image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum303.64542
5-th percentile835.80904
Q11625.2658
median3091
Q35948.4546
95-th percentile10812.433
Maximum15204.633
Range14900.988
Interquartile range (IQR)4323.1888

Descriptive statistics

Standard deviation3180.4897
Coefficient of variation (CV)0.75904589
Kurtosis0.61802104
Mean4190.1151
Median Absolute Deviation (MAD)1754.03
Skewness1.1286314
Sum5.3423968 × 108
Variance10115514
MonotonicityNot monotonic
2023-04-06T09:09:59.392816image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2295.058333 22
 
< 0.1%
6082.1875 22
 
< 0.1%
3080.555 21
 
< 0.1%
6358.956667 21
 
< 0.1%
6639.56 20
 
< 0.1%
4387.2725 20
 
< 0.1%
5766.491667 20
 
< 0.1%
1315.560833 19
 
< 0.1%
6769.13 19
 
< 0.1%
536.43125 19
 
< 0.1%
Other values (13673) 127297
84.9%
(Missing) 22500
 
15.0%
ValueCountFrequency (%)
303.6454167 10
< 0.1%
319.55625 11
< 0.1%
331.0319233 2
 
< 0.1%
332.1283333 10
< 0.1%
332.43125 10
< 0.1%
333.5966667 10
< 0.1%
355.2083333 12
< 0.1%
357.2558333 11
< 0.1%
358.0583333 10
< 0.1%
361.6033333 10
< 0.1%
ValueCountFrequency (%)
15204.63333 10
< 0.1%
15167.18 12
< 0.1%
15136.69667 10
< 0.1%
15115.19 10
< 0.1%
15101.94 11
< 0.1%
15091.08667 5
< 0.1%
15090.07667 11
< 0.1%
15066.78333 11
< 0.1%
15038.31667 5
< 0.1%
14978.33667 10
< 0.1%

Num_Bank_Accounts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1183
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.00694
Minimum-1
Maximum1798
Zeros6494
Zeros (%)4.3%
Negative37
Negative (%)< 0.1%
Memory size1.1 MiB
2023-04-06T09:09:59.583141image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median6
Q37
95-th percentile10
Maximum1798
Range1799
Interquartile range (IQR)4

Descriptive statistics

Standard deviation117.06948
Coefficient of variation (CV)6.8836296
Kurtosis132.64797
Mean17.00694
Median Absolute Deviation (MAD)2
Skewness11.218773
Sum2551041
Variance13705.262
MonotonicityNot monotonic
2023-04-06T09:09:59.755144image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 19505
13.0%
7 19231
12.8%
8 19152
12.8%
4 18286
12.2%
5 18186
12.1%
3 17905
11.9%
9 8181
5.5%
10 7846
5.2%
1 6743
 
4.5%
0 6494
 
4.3%
Other values (1173) 8471
5.6%
ValueCountFrequency (%)
-1 37
 
< 0.1%
0 6494
 
4.3%
1 6743
 
4.5%
2 6456
 
4.3%
3 17905
11.9%
4 18286
12.2%
5 18186
12.1%
6 19505
13.0%
7 19231
12.8%
8 19152
12.8%
ValueCountFrequency (%)
1798 3
< 0.1%
1794 2
< 0.1%
1793 1
 
< 0.1%
1789 2
< 0.1%
1786 1
 
< 0.1%
1784 2
< 0.1%
1783 2
< 0.1%
1782 1
 
< 0.1%
1781 1
 
< 0.1%
1780 1
 
< 0.1%

Num_Credit_Card
Real number (ℝ)

Distinct1344
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.623447
Minimum0
Maximum1499
Zeros29
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-06T09:09:59.951146image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median5
Q37
95-th percentile10
Maximum1499
Range1499
Interquartile range (IQR)3

Descriptive statistics

Standard deviation129.14301
Coefficient of variation (CV)5.7083701
Kurtosis73.645489
Mean22.623447
Median Absolute Deviation (MAD)2
Skewness8.4006471
Sum3393517
Variance16677.916
MonotonicityNot monotonic
2023-04-06T09:10:00.128108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 27669
18.4%
7 24886
16.6%
6 24802
16.5%
4 21102
14.1%
3 19816
13.2%
8 7453
 
5.0%
10 7265
 
4.8%
9 6976
 
4.7%
2 3280
 
2.2%
1 3195
 
2.1%
Other values (1334) 3556
 
2.4%
ValueCountFrequency (%)
0 29
 
< 0.1%
1 3195
 
2.1%
2 3280
 
2.2%
3 19816
13.2%
4 21102
14.1%
5 27669
18.4%
6 24802
16.5%
7 24886
16.6%
8 7453
 
5.0%
9 6976
 
4.7%
ValueCountFrequency (%)
1499 3
< 0.1%
1498 5
< 0.1%
1497 3
< 0.1%
1496 2
 
< 0.1%
1495 2
 
< 0.1%
1494 1
 
< 0.1%
1493 2
 
< 0.1%
1492 2
 
< 0.1%
1491 1
 
< 0.1%
1490 2
 
< 0.1%

Interest_Rate
Real number (ℝ)

Distinct2394
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.234907
Minimum1
Maximum5799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-06T09:10:00.324157image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median13
Q320
95-th percentile32
Maximum5799
Range5798
Interquartile range (IQR)12

Descriptive statistics

Standard deviation461.53719
Coefficient of variation (CV)6.4790875
Kurtosis87.492459
Mean71.234907
Median Absolute Deviation (MAD)6
Skewness9.1228759
Sum10685236
Variance213016.58
MonotonicityNot monotonic
2023-04-06T09:10:00.497167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 7515
 
5.0%
5 7479
 
5.0%
6 7089
 
4.7%
12 6828
 
4.6%
10 6799
 
4.5%
9 6747
 
4.5%
7 6744
 
4.5%
11 6626
 
4.4%
18 6154
 
4.1%
15 5984
 
4.0%
Other values (2384) 82035
54.7%
ValueCountFrequency (%)
1 4027
2.7%
2 3710
2.5%
3 4153
2.8%
4 3876
2.6%
5 7479
5.0%
6 7089
4.7%
7 6744
4.5%
8 7515
5.0%
9 6747
4.5%
10 6799
4.5%
ValueCountFrequency (%)
5799 1
< 0.1%
5797 1
< 0.1%
5792 1
< 0.1%
5789 1
< 0.1%
5788 1
< 0.1%
5776 1
< 0.1%
5775 1
< 0.1%
5774 1
< 0.1%
5773 2
< 0.1%
5771 1
< 0.1%

Num_of_Loan
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct623
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
3
21500 
2
21423 
4
20998 
0
15543 
1
15112 
Other values (618)
55424 

Length

Max length5
Median length1
Mean length1.1762333
Min length1

Characters and Unicode

Total characters176435
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique489 ?
Unique (%)0.3%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3 21500
14.3%
2 21423
14.3%
4 20998
14.0%
0 15543
10.4%
1 15112
10.1%
6 11112
7.4%
7 10413
6.9%
5 10302
6.9%
-100 5850
 
3.9%
9 5288
 
3.5%
Other values (613) 12459
8.3%

Length

2023-04-06T09:10:00.696255image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3 22618
15.1%
2 22547
15.0%
4 22111
14.7%
0 16376
10.9%
1 15901
10.6%
6 11705
7.8%
7 11024
7.3%
5 10814
7.2%
100 5851
 
3.9%
9 5539
 
3.7%
Other values (589) 5514
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 28253
16.0%
3 22866
13.0%
2 22796
12.9%
4 22375
12.7%
1 22250
12.6%
6 11891
6.7%
7 11198
 
6.3%
5 11023
 
6.2%
_ 7221
 
4.1%
- 5850
 
3.3%
Other values (2) 10712
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 163364
92.6%
Connector Punctuation 7221
 
4.1%
Dash Punctuation 5850
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28253
17.3%
3 22866
14.0%
2 22796
14.0%
4 22375
13.7%
1 22250
13.6%
6 11891
7.3%
7 11198
 
6.9%
5 11023
 
6.7%
9 5747
 
3.5%
8 4965
 
3.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7221
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5850
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176435
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28253
16.0%
3 22866
13.0%
2 22796
12.9%
4 22375
12.7%
1 22250
12.6%
6 11891
6.7%
7 11198
 
6.3%
5 11023
 
6.2%
_ 7221
 
4.1%
- 5850
 
3.3%
Other values (2) 10712
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176435
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28253
16.0%
3 22866
13.0%
2 22796
12.9%
4 22375
12.7%
1 22250
12.6%
6 11891
6.7%
7 11198
 
6.3%
5 11023
 
6.2%
_ 7221
 
4.1%
- 5850
 
3.3%
Other values (2) 10712
 
6.1%

Type_of_Loan
Categorical

HIGH CARDINALITY  MISSING 

Distinct6260
Distinct (%)4.7%
Missing17112
Missing (%)11.4%
Memory size1.1 MiB
Not Specified
 
2112
Credit-Builder Loan
 
1920
Personal Loan
 
1908
Debt Consolidation Loan
 
1896
Student Loan
 
1860
Other values (6255)
123192 

Length

Max length182
Median length142
Mean length66.683583
Min length9

Characters and Unicode

Total characters8861448
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
2nd rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
3rd rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
4th rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
5th rowCredit-Builder Loan

Common Values

ValueCountFrequency (%)
Not Specified 2112
 
1.4%
Credit-Builder Loan 1920
 
1.3%
Personal Loan 1908
 
1.3%
Debt Consolidation Loan 1896
 
1.3%
Student Loan 1860
 
1.2%
Payday Loan 1800
 
1.2%
Mortgage Loan 1764
 
1.2%
Auto Loan 1728
 
1.2%
Home Equity Loan 1704
 
1.1%
Personal Loan, and Student Loan 480
 
0.3%
Other values (6250) 115716
77.1%
(Missing) 17112
 
11.4%

Length

2023-04-06T09:10:00.902951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
loan 470508
36.4%
and 116196
 
9.0%
payday 60852
 
4.7%
credit-builder 60660
 
4.7%
not 59424
 
4.6%
specified 59424
 
4.6%
home 58656
 
4.5%
equity 58656
 
4.5%
student 58452
 
4.5%
mortgage 58404
 
4.5%
Other values (4) 231648
17.9%

Most occurring characters

ValueCountFrequency (%)
1159992
13.1%
o 936804
10.6%
a 883308
 
10.0%
n 819816
 
9.3%
e 532176
 
6.0%
t 527364
 
6.0%
d 474408
 
5.4%
L 470508
 
5.3%
i 415152
 
4.7%
, 397044
 
4.5%
Other values (23) 2244876
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6006408
67.8%
Uppercase Letter 1237344
 
14.0%
Space Separator 1159992
 
13.1%
Other Punctuation 397044
 
4.5%
Dash Punctuation 60660
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 936804
15.6%
a 883308
14.7%
n 819816
13.6%
e 532176
8.9%
t 527364
8.8%
d 474408
7.9%
i 415152
6.9%
r 238056
 
4.0%
u 234756
 
3.9%
y 180360
 
3.0%
Other values (9) 764208
12.7%
Uppercase Letter
ValueCountFrequency (%)
L 470508
38.0%
P 119184
 
9.6%
C 118824
 
9.6%
S 117876
 
9.5%
B 60660
 
4.9%
N 59424
 
4.8%
H 58656
 
4.7%
E 58656
 
4.7%
M 58404
 
4.7%
D 58164
 
4.7%
Space Separator
ValueCountFrequency (%)
1159992
100.0%
Other Punctuation
ValueCountFrequency (%)
, 397044
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60660
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7243752
81.7%
Common 1617696
 
18.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 936804
12.9%
a 883308
12.2%
n 819816
11.3%
e 532176
 
7.3%
t 527364
 
7.3%
d 474408
 
6.5%
L 470508
 
6.5%
i 415152
 
5.7%
r 238056
 
3.3%
u 234756
 
3.2%
Other values (20) 1711404
23.6%
Common
ValueCountFrequency (%)
1159992
71.7%
, 397044
 
24.5%
- 60660
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8861448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1159992
13.1%
o 936804
10.6%
a 883308
 
10.0%
n 819816
 
9.3%
e 532176
 
6.0%
t 527364
 
6.0%
d 474408
 
5.4%
L 470508
 
5.3%
i 415152
 
4.7%
, 397044
 
4.5%
Other values (23) 2244876
25.3%

Delay_from_due_date
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.0634
Minimum-5
Maximum67
Zeros1821
Zeros (%)1.2%
Negative889
Negative (%)0.6%
Memory size1.1 MiB
2023-04-06T09:10:01.102555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile3
Q110
median18
Q328
95-th percentile54
Maximum67
Range72
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.860154
Coefficient of variation (CV)0.70549647
Kurtosis0.3469547
Mean21.0634
Median Absolute Deviation (MAD)9
Skewness0.96589567
Sum3159510
Variance220.82419
MonotonicityNot monotonic
2023-04-06T09:10:01.272962image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 5355
 
3.6%
13 5185
 
3.5%
8 5004
 
3.3%
14 4949
 
3.3%
10 4926
 
3.3%
9 4889
 
3.3%
7 4821
 
3.2%
12 4766
 
3.2%
11 4755
 
3.2%
6 4721
 
3.1%
Other values (63) 100629
67.1%
ValueCountFrequency (%)
-5 51
 
< 0.1%
-4 111
 
0.1%
-3 177
 
0.1%
-2 239
 
0.2%
-1 311
 
0.2%
0 1821
1.2%
1 1994
1.3%
2 2011
1.3%
3 2534
1.7%
4 2547
1.7%
ValueCountFrequency (%)
67 29
 
< 0.1%
66 44
 
< 0.1%
65 86
 
0.1%
64 97
 
0.1%
63 90
 
0.1%
62 824
0.5%
61 785
0.5%
60 792
0.5%
59 757
0.5%
58 835
0.6%

Num_of_Delayed_Payment
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct1058
Distinct (%)0.8%
Missing10500
Missing (%)7.0%
Memory size1.1 MiB
19
 
7949
17
 
7806
16
 
7721
15
 
7671
10
 
7670
Other values (1053)
100683 

Length

Max length5
Median length2
Mean length1.7707097
Min length1

Characters and Unicode

Total characters247014
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique867 ?
Unique (%)0.6%

Sample

1st row7
2nd row9
3rd row4
4th row5
5th row1

Common Values

ValueCountFrequency (%)
19 7949
 
5.3%
17 7806
 
5.2%
16 7721
 
5.1%
15 7671
 
5.1%
10 7670
 
5.1%
18 7653
 
5.1%
12 7388
 
4.9%
20 7357
 
4.9%
9 7199
 
4.8%
11 7107
 
4.7%
Other values (1048) 63979
42.7%
(Missing) 10500
 
7.0%

Length

2023-04-06T09:10:01.461010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19 8188
 
5.9%
17 8048
 
5.8%
16 7949
 
5.7%
15 7911
 
5.7%
10 7900
 
5.7%
18 7847
 
5.6%
12 7622
 
5.5%
20 7607
 
5.5%
9 7421
 
5.3%
11 7314
 
5.2%
Other values (1001) 61693
44.2%

Most occurring characters

ValueCountFrequency (%)
1 89916
36.4%
2 38954
15.8%
0 18253
 
7.4%
9 15941
 
6.5%
8 15700
 
6.4%
5 13902
 
5.6%
3 12780
 
5.2%
7 12291
 
5.0%
6 12184
 
4.9%
4 11991
 
4.9%
Other values (2) 5102
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 241912
97.9%
Connector Punctuation 4171
 
1.7%
Dash Punctuation 931
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 89916
37.2%
2 38954
16.1%
0 18253
 
7.5%
9 15941
 
6.6%
8 15700
 
6.5%
5 13902
 
5.7%
3 12780
 
5.3%
7 12291
 
5.1%
6 12184
 
5.0%
4 11991
 
5.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4171
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 931
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 247014
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 89916
36.4%
2 38954
15.8%
0 18253
 
7.4%
9 15941
 
6.5%
8 15700
 
6.4%
5 13902
 
5.6%
3 12780
 
5.2%
7 12291
 
5.0%
6 12184
 
4.9%
4 11991
 
4.9%
Other values (2) 5102
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 247014
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 89916
36.4%
2 38954
15.8%
0 18253
 
7.4%
9 15941
 
6.5%
8 15700
 
6.4%
5 13902
 
5.6%
3 12780
 
5.2%
7 12291
 
5.0%
6 12184
 
4.9%
4 11991
 
4.9%
Other values (2) 5102
 
2.1%
Distinct4605
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
_
 
3150
11.5
 
197
8.22
 
189
11.32
 
189
7.35
 
181
Other values (4600)
146094 

Length

Max length21
Median length20
Mean length4.70556
Min length1

Characters and Unicode

Total characters705834
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique480 ?
Unique (%)0.3%

Sample

1st row11.27
2nd row13.27
3rd row12.27
4th row11.27
5th row5.42

Common Values

ValueCountFrequency (%)
_ 3150
 
2.1%
11.5 197
 
0.1%
8.22 189
 
0.1%
11.32 189
 
0.1%
7.35 181
 
0.1%
10.06 178
 
0.1%
8.23 169
 
0.1%
7.69 166
 
0.1%
7.01 165
 
0.1%
11.49 164
 
0.1%
Other values (4595) 145252
96.8%

Length

2023-04-06T09:10:01.649633image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3150
 
2.1%
11.5 197
 
0.1%
8.22 189
 
0.1%
11.32 189
 
0.1%
7.35 181
 
0.1%
10.06 178
 
0.1%
8.23 169
 
0.1%
3.93 168
 
0.1%
7.69 166
 
0.1%
7.01 165
 
0.1%
Other values (3879) 145248
96.8%

Most occurring characters

ValueCountFrequency (%)
. 146850
20.8%
1 103312
14.6%
9 69985
9.9%
0 59693
8.5%
2 54609
 
7.7%
7 45960
 
6.5%
8 45785
 
6.5%
5 44455
 
6.3%
6 43906
 
6.2%
3 43013
 
6.1%
Other values (3) 48266
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 553413
78.4%
Other Punctuation 146850
 
20.8%
Connector Punctuation 3150
 
0.4%
Dash Punctuation 2421
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 103312
18.7%
9 69985
12.6%
0 59693
10.8%
2 54609
9.9%
7 45960
8.3%
8 45785
8.3%
5 44455
8.0%
6 43906
7.9%
3 43013
7.8%
4 42695
7.7%
Other Punctuation
ValueCountFrequency (%)
. 146850
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3150
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2421
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 705834
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 146850
20.8%
1 103312
14.6%
9 69985
9.9%
0 59693
8.5%
2 54609
 
7.7%
7 45960
 
6.5%
8 45785
 
6.5%
5 44455
 
6.3%
6 43906
 
6.2%
3 43013
 
6.1%
Other values (3) 48266
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 705834
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 146850
20.8%
1 103312
14.6%
9 69985
9.9%
0 59693
8.5%
2 54609
 
7.7%
7 45960
 
6.5%
8 45785
 
6.5%
5 44455
 
6.3%
6 43906
 
6.2%
3 43013
 
6.1%
Other values (3) 48266
 
6.8%

Num_Credit_Inquiries
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1607
Distinct (%)1.1%
Missing3000
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean28.529014
Minimum0
Maximum2597
Zeros8074
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-06T09:10:01.827371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q39
95-th percentile14
Maximum2597
Range2597
Interquartile range (IQR)6

Descriptive statistics

Standard deviation194.45606
Coefficient of variation (CV)6.8160807
Kurtosis99.144085
Mean28.529014
Median Absolute Deviation (MAD)3
Skewness9.7183174
Sum4193765
Variance37813.158
MonotonicityNot monotonic
2023-04-06T09:10:02.006994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 15673
10.4%
6 12486
 
8.3%
3 12356
 
8.2%
7 12353
 
8.2%
8 11788
 
7.9%
2 10482
 
7.0%
5 10402
 
6.9%
1 9335
 
6.2%
9 8806
 
5.9%
0 8074
 
5.4%
Other values (1597) 35245
23.5%
ValueCountFrequency (%)
0 8074
5.4%
1 9335
6.2%
2 10482
7.0%
3 12356
8.2%
4 15673
10.4%
5 10402
6.9%
6 12486
8.3%
7 12353
8.2%
8 11788
7.9%
9 8806
5.9%
ValueCountFrequency (%)
2597 1
 
< 0.1%
2594 1
 
< 0.1%
2593 1
 
< 0.1%
2592 3
< 0.1%
2589 2
< 0.1%
2588 2
< 0.1%
2587 1
 
< 0.1%
2586 2
< 0.1%
2583 2
< 0.1%
2580 1
 
< 0.1%

Credit_Mix
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Standard
54858 
Good
36597 
_
30000 
Bad
28545 

Length

Max length8
Median length4
Mean length4.67258
Min length1

Characters and Unicode

Total characters700887
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowGood
3rd rowGood
4th rowGood
5th rowGood

Common Values

ValueCountFrequency (%)
Standard 54858
36.6%
Good 36597
24.4%
_ 30000
20.0%
Bad 28545
19.0%

Length

2023-04-06T09:10:02.198993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-06T09:10:02.375033image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
standard 54858
36.6%
good 36597
24.4%
30000
20.0%
bad 28545
19.0%

Most occurring characters

ValueCountFrequency (%)
d 174858
24.9%
a 138261
19.7%
o 73194
10.4%
S 54858
 
7.8%
t 54858
 
7.8%
n 54858
 
7.8%
r 54858
 
7.8%
G 36597
 
5.2%
_ 30000
 
4.3%
B 28545
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 550887
78.6%
Uppercase Letter 120000
 
17.1%
Connector Punctuation 30000
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 174858
31.7%
a 138261
25.1%
o 73194
13.3%
t 54858
 
10.0%
n 54858
 
10.0%
r 54858
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
S 54858
45.7%
G 36597
30.5%
B 28545
23.8%
Connector Punctuation
ValueCountFrequency (%)
_ 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 670887
95.7%
Common 30000
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 174858
26.1%
a 138261
20.6%
o 73194
10.9%
S 54858
 
8.2%
t 54858
 
8.2%
n 54858
 
8.2%
r 54858
 
8.2%
G 36597
 
5.5%
B 28545
 
4.3%
Common
ValueCountFrequency (%)
_ 30000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700887
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 174858
24.9%
a 138261
19.7%
o 73194
10.4%
S 54858
 
7.8%
t 54858
 
7.8%
n 54858
 
7.8%
r 54858
 
7.8%
G 36597
 
5.2%
_ 30000
 
4.3%
B 28545
 
4.1%

Outstanding_Debt
Categorical

Distinct13622
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1360.45
 
36
1151.7
 
35
1109.03
 
35
460.46
 
35
935.74
 
24
Other values (13617)
149835 

Length

Max length8
Median length7
Mean length6.4332
Min length3

Characters and Unicode

Total characters964980
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1340 ?
Unique (%)0.9%

Sample

1st row809.98
2nd row809.98
3rd row809.98
4th row809.98
5th row605.03

Common Values

ValueCountFrequency (%)
1360.45 36
 
< 0.1%
1151.7 35
 
< 0.1%
1109.03 35
 
< 0.1%
460.46 35
 
< 0.1%
935.74 24
 
< 0.1%
1292.14 24
 
< 0.1%
1454.68 24
 
< 0.1%
1024.56 24
 
< 0.1%
438.75 24
 
< 0.1%
1072.42 24
 
< 0.1%
Other values (13612) 149715
99.8%

Length

2023-04-06T09:10:02.527032image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1360.45 36
 
< 0.1%
1109.03 36
 
< 0.1%
460.46 36
 
< 0.1%
1151.7 36
 
< 0.1%
1464.16 24
 
< 0.1%
1194.38 24
 
< 0.1%
10.29 24
 
< 0.1%
585.77 24
 
< 0.1%
462.11 24
 
< 0.1%
969.19 24
 
< 0.1%
Other values (12193) 149712
99.8%

Most occurring characters

ValueCountFrequency (%)
. 150000
15.5%
1 125352
13.0%
2 95904
9.9%
3 88260
9.1%
4 87528
9.1%
5 74196
7.7%
6 73368
7.6%
8 72012
7.5%
7 71496
7.4%
9 70716
7.3%
Other values (2) 56148
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 813480
84.3%
Other Punctuation 150000
 
15.5%
Connector Punctuation 1500
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 125352
15.4%
2 95904
11.8%
3 88260
10.8%
4 87528
10.8%
5 74196
9.1%
6 73368
9.0%
8 72012
8.9%
7 71496
8.8%
9 70716
8.7%
0 54648
6.7%
Other Punctuation
ValueCountFrequency (%)
. 150000
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 964980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 150000
15.5%
1 125352
13.0%
2 95904
9.9%
3 88260
9.1%
4 87528
9.1%
5 74196
7.7%
6 73368
7.6%
8 72012
7.5%
7 71496
7.4%
9 70716
7.3%
Other values (2) 56148
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 964980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 150000
15.5%
1 125352
13.0%
2 95904
9.9%
3 88260
9.1%
4 87528
9.1%
5 74196
7.7%
6 73368
7.6%
8 72012
7.5%
7 71496
7.4%
9 70716
7.3%
Other values (2) 56148
 
5.8%

Credit_Utilization_Ratio
Real number (ℝ)

Distinct150000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.283309
Minimum20
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-06T09:10:02.687009image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24.247778
Q128.054731
median32.297058
Q336.487954
95-th percentile40.228918
Maximum50
Range30
Interquartile range (IQR)8.4332227

Descriptive statistics

Standard deviation5.1133154
Coefficient of variation (CV)0.15838883
Kurtosis-0.94582088
Mean32.283309
Median Absolute Deviation (MAD)4.2156115
Skewness0.031599852
Sum4842496.3
Variance26.145994
MonotonicityNot monotonic
2023-04-06T09:10:02.851774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.03040186 1
 
< 0.1%
26.91679533 1
 
< 0.1%
27.73605126 1
 
< 0.1%
40.98413994 1
 
< 0.1%
26.12971783 1
 
< 0.1%
24.78390632 1
 
< 0.1%
26.65025816 1
 
< 0.1%
23.86424414 1
 
< 0.1%
29.63812969 1
 
< 0.1%
31.87539864 1
 
< 0.1%
Other values (149990) 149990
> 99.9%
ValueCountFrequency (%)
20 1
< 0.1%
20.10076996 1
< 0.1%
20.1729419 1
< 0.1%
20.24413035 1
< 0.1%
20.25707336 1
< 0.1%
20.50965206 1
< 0.1%
20.62001732 1
< 0.1%
20.71974515 1
< 0.1%
20.73922549 1
< 0.1%
20.80058685 1
< 0.1%
ValueCountFrequency (%)
50 1
< 0.1%
49.56451935 1
< 0.1%
49.5223243 1
< 0.1%
49.25498298 1
< 0.1%
49.06427745 1
< 0.1%
48.54066309 1
< 0.1%
48.48985173 1
< 0.1%
48.33729091 1
< 0.1%
48.24700252 1
< 0.1%
48.22871401 1
< 0.1%

Credit_History_Age
Categorical

HIGH CARDINALITY  MISSING 

Distinct408
Distinct (%)0.3%
Missing13500
Missing (%)9.0%
Memory size1.1 MiB
17 Years and 11 Months
 
628
18 Years and 4 Months
 
621
18 Years and 3 Months
 
617
19 Years and 9 Months
 
615
18 Years and 2 Months
 
615
Other values (403)
133404 

Length

Max length22
Median length21
Mean length20.982945
Min length20

Characters and Unicode

Total characters2864172
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22 Years and 9 Months
2nd row22 Years and 10 Months
3rd row23 Years and 0 Months
4th row27 Years and 3 Months
5th row27 Years and 4 Months

Common Values

ValueCountFrequency (%)
17 Years and 11 Months 628
 
0.4%
18 Years and 4 Months 621
 
0.4%
18 Years and 3 Months 617
 
0.4%
19 Years and 9 Months 615
 
0.4%
18 Years and 2 Months 615
 
0.4%
16 Years and 2 Months 612
 
0.4%
18 Years and 1 Months 612
 
0.4%
16 Years and 1 Months 610
 
0.4%
18 Years and 0 Months 609
 
0.4%
19 Years and 5 Months 608
 
0.4%
Other values (398) 130353
86.9%
(Missing) 13500
 
9.0%

Length

2023-04-06T09:10:03.036750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 136500
20.0%
and 136500
20.0%
months 136500
20.0%
6 15883
 
2.3%
8 15600
 
2.3%
9 15544
 
2.3%
10 15410
 
2.3%
11 15400
 
2.3%
7 15268
 
2.2%
5 13893
 
2.0%
Other values (28) 166002
24.3%

Most occurring characters

ValueCountFrequency (%)
546000
19.1%
a 273000
9.5%
s 273000
9.5%
n 273000
9.5%
o 136500
 
4.8%
t 136500
 
4.8%
Y 136500
 
4.8%
e 136500
 
4.8%
r 136500
 
4.8%
d 136500
 
4.8%
Other values (12) 680172
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1638000
57.2%
Space Separator 546000
 
19.1%
Decimal Number 407172
 
14.2%
Uppercase Letter 273000
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 113106
27.8%
2 67564
16.6%
3 39070
 
9.6%
0 37297
 
9.2%
6 26941
 
6.6%
9 26898
 
6.6%
8 26897
 
6.6%
7 26132
 
6.4%
5 22715
 
5.6%
4 20552
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
a 273000
16.7%
s 273000
16.7%
n 273000
16.7%
o 136500
8.3%
t 136500
8.3%
e 136500
8.3%
r 136500
8.3%
d 136500
8.3%
h 136500
8.3%
Uppercase Letter
ValueCountFrequency (%)
Y 136500
50.0%
M 136500
50.0%
Space Separator
ValueCountFrequency (%)
546000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1911000
66.7%
Common 953172
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
546000
57.3%
1 113106
 
11.9%
2 67564
 
7.1%
3 39070
 
4.1%
0 37297
 
3.9%
6 26941
 
2.8%
9 26898
 
2.8%
8 26897
 
2.8%
7 26132
 
2.7%
5 22715
 
2.4%
Latin
ValueCountFrequency (%)
a 273000
14.3%
s 273000
14.3%
n 273000
14.3%
o 136500
7.1%
t 136500
7.1%
Y 136500
7.1%
e 136500
7.1%
r 136500
7.1%
d 136500
7.1%
M 136500
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2864172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
546000
19.1%
a 273000
9.5%
s 273000
9.5%
n 273000
9.5%
o 136500
 
4.8%
t 136500
 
4.8%
Y 136500
 
4.8%
e 136500
 
4.8%
r 136500
 
4.8%
d 136500
 
4.8%
Other values (12) 680172
23.7%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Yes
78484 
No
53516 
NM
18000 

Length

Max length3
Median length3
Mean length2.5232267
Min length2

Characters and Unicode

Total characters378484
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
Yes 78484
52.3%
No 53516
35.7%
NM 18000
 
12.0%

Length

2023-04-06T09:10:03.204756image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-06T09:10:03.362993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
yes 78484
52.3%
no 53516
35.7%
nm 18000
 
12.0%

Most occurring characters

ValueCountFrequency (%)
Y 78484
20.7%
e 78484
20.7%
s 78484
20.7%
N 71516
18.9%
o 53516
14.1%
M 18000
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 210484
55.6%
Uppercase Letter 168000
44.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 78484
46.7%
N 71516
42.6%
M 18000
 
10.7%
Lowercase Letter
ValueCountFrequency (%)
e 78484
37.3%
s 78484
37.3%
o 53516
25.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 378484
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 78484
20.7%
e 78484
20.7%
s 78484
20.7%
N 71516
18.9%
o 53516
14.1%
M 18000
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 378484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 78484
20.7%
e 78484
20.7%
s 78484
20.7%
N 71516
18.9%
o 53516
14.1%
M 18000
 
4.8%

Total_EMI_per_month
Real number (ℝ)

Distinct16960
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1432.5136
Minimum0
Maximum82398
Zeros15615
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-06T09:10:03.530271image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130.947775
median71.280006
Q3166.27956
95-th percentile499.73202
Maximum82398
Range82398
Interquartile range (IQR)135.33178

Descriptive statistics

Standard deviation8403.76
Coefficient of variation (CV)5.8664435
Kurtosis51.398183
Mean1432.5136
Median Absolute Deviation (MAD)51.776274
Skewness7.0497762
Sum2.1487704 × 108
Variance70623182
MonotonicityNot monotonic
2023-04-06T09:10:03.701304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15615
 
10.4%
49.57494921 12
 
< 0.1%
27.97205372 12
 
< 0.1%
48.84009702 12
 
< 0.1%
24.25711798 12
 
< 0.1%
111.6372765 12
 
< 0.1%
212.4293521 12
 
< 0.1%
18.69416213 12
 
< 0.1%
126.5799472 12
 
< 0.1%
203.6057951 12
 
< 0.1%
Other values (16950) 134277
89.5%
ValueCountFrequency (%)
0 15615
10.4%
4.462837467 12
 
< 0.1%
4.713183572 12
 
< 0.1%
4.865689677 12
 
< 0.1%
4.916138542 12
 
< 0.1%
5.138484696 12
 
< 0.1%
5.218466359 12
 
< 0.1%
5.24927327 11
 
< 0.1%
5.262291048 12
 
< 0.1%
5.351086151 11
 
< 0.1%
ValueCountFrequency (%)
82398 1
< 0.1%
82347 1
< 0.1%
82331 1
< 0.1%
82316 1
< 0.1%
82256 1
< 0.1%
82248 1
< 0.1%
82236 1
< 0.1%
82235 1
< 0.1%
82225 1
< 0.1%
82204 1
< 0.1%

Amount_invested_monthly
Categorical

HIGH CARDINALITY  MISSING 

Distinct136497
Distinct (%)95.3%
Missing6750
Missing (%)4.5%
Memory size1.1 MiB
__10000__
 
6480
0.0
 
275
79.77734815487014
 
1
70.78372395611446
 
1
36.319514426769054
 
1
Other values (136492)
136492 

Length

Max length18
Median length17
Mean length16.962729
Min length3

Characters and Unicode

Total characters2429911
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique136495 ?
Unique (%)95.3%

Sample

1st row236.64268203272135
2nd row21.465380264657146
3rd row148.23393788500925
4th row39.08251089460281
5th row39.684018417945296

Common Values

ValueCountFrequency (%)
__10000__ 6480
 
4.3%
0.0 275
 
0.2%
79.77734815487014 1
 
< 0.1%
70.78372395611446 1
 
< 0.1%
36.319514426769054 1
 
< 0.1%
152.64729262606082 1
 
< 0.1%
25.795644267454087 1
 
< 0.1%
238.46444826179817 1
 
< 0.1%
236.64268203272135 1
 
< 0.1%
199.24670014227908 1
 
< 0.1%
Other values (136487) 136487
91.0%
(Missing) 6750
 
4.5%

Length

2023-04-06T09:10:03.896282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10000 6480
 
4.5%
0.0 275
 
0.2%
177.95183568608738 1
 
< 0.1%
181.0072171318061 1
 
< 0.1%
251.62736875017606 1
 
< 0.1%
72.68014533363515 1
 
< 0.1%
153.53448761392985 1
 
< 0.1%
397.50365354404653 1
 
< 0.1%
453.6151305781054 1
 
< 0.1%
841.2322359154716 1
 
< 0.1%
Other values (136487) 136487
95.3%

Most occurring characters

ValueCountFrequency (%)
1 259899
10.7%
2 234298
9.6%
4 227447
9.4%
3 226553
9.3%
0 226115
9.3%
5 224410
9.2%
6 223483
9.2%
8 217962
9.0%
7 217847
9.0%
9 209207
8.6%
Other values (2) 162690
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2267221
93.3%
Other Punctuation 136770
 
5.6%
Connector Punctuation 25920
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 259899
11.5%
2 234298
10.3%
4 227447
10.0%
3 226553
10.0%
0 226115
10.0%
5 224410
9.9%
6 223483
9.9%
8 217962
9.6%
7 217847
9.6%
9 209207
9.2%
Other Punctuation
ValueCountFrequency (%)
. 136770
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 25920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2429911
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 259899
10.7%
2 234298
9.6%
4 227447
9.4%
3 226553
9.3%
0 226115
9.3%
5 224410
9.2%
6 223483
9.2%
8 217962
9.0%
7 217847
9.0%
9 209207
8.6%
Other values (2) 162690
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2429911
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 259899
10.7%
2 234298
9.6%
4 227447
9.4%
3 226553
9.3%
0 226115
9.3%
5 224410
9.2%
6 223483
9.2%
8 217962
9.0%
7 217847
9.0%
9 209207
8.6%
Other values (2) 162690
6.7%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Low_spent_Small_value_payments
38207 
High_spent_Medium_value_payments
26462 
Low_spent_Medium_value_payments
20698 
High_spent_Large_value_payments
20565 
High_spent_Small_value_payments
16991 
Other values (2)
27077 

Length

Max length32
Median length31
Mean length28.917187
Min length6

Characters and Unicode

Total characters4337578
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow_spent_Small_value_payments
2nd rowHigh_spent_Medium_value_payments
3rd rowLow_spent_Medium_value_payments
4th rowHigh_spent_Medium_value_payments
5th rowHigh_spent_Large_value_payments

Common Values

ValueCountFrequency (%)
Low_spent_Small_value_payments 38207
25.5%
High_spent_Medium_value_payments 26462
17.6%
Low_spent_Medium_value_payments 20698
13.8%
High_spent_Large_value_payments 20565
13.7%
High_spent_Small_value_payments 16991
11.3%
Low_spent_Large_value_payments 15677
10.5%
!@9#%8 11400
 
7.6%

Length

2023-04-06T09:10:04.066642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-06T09:10:04.269081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
low_spent_small_value_payments 38207
25.5%
high_spent_medium_value_payments 26462
17.6%
low_spent_medium_value_payments 20698
13.8%
high_spent_large_value_payments 20565
13.7%
high_spent_small_value_payments 16991
11.3%
low_spent_large_value_payments 15677
10.5%
9#%8 11400
 
7.6%

Most occurring characters

ValueCountFrequency (%)
_ 554400
12.8%
e 499202
11.5%
a 368640
 
8.5%
s 277200
 
6.4%
p 277200
 
6.4%
n 277200
 
6.4%
t 277200
 
6.4%
l 248996
 
5.7%
m 240958
 
5.6%
u 185760
 
4.3%
Other values (19) 1130822
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3437578
79.3%
Connector Punctuation 554400
 
12.8%
Uppercase Letter 277200
 
6.4%
Other Punctuation 45600
 
1.1%
Decimal Number 22800
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 499202
14.5%
a 368640
10.7%
s 277200
8.1%
p 277200
8.1%
n 277200
8.1%
t 277200
8.1%
l 248996
 
7.2%
m 240958
 
7.0%
u 185760
 
5.4%
v 138600
 
4.0%
Other values (8) 646622
18.8%
Uppercase Letter
ValueCountFrequency (%)
L 110824
40.0%
H 64018
23.1%
S 55198
19.9%
M 47160
17.0%
Other Punctuation
ValueCountFrequency (%)
! 11400
25.0%
@ 11400
25.0%
# 11400
25.0%
% 11400
25.0%
Decimal Number
ValueCountFrequency (%)
9 11400
50.0%
8 11400
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 554400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3714778
85.6%
Common 622800
 
14.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 499202
13.4%
a 368640
 
9.9%
s 277200
 
7.5%
p 277200
 
7.5%
n 277200
 
7.5%
t 277200
 
7.5%
l 248996
 
6.7%
m 240958
 
6.5%
u 185760
 
5.0%
v 138600
 
3.7%
Other values (12) 923822
24.9%
Common
ValueCountFrequency (%)
_ 554400
89.0%
! 11400
 
1.8%
@ 11400
 
1.8%
9 11400
 
1.8%
# 11400
 
1.8%
% 11400
 
1.8%
8 11400
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4337578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 554400
12.8%
e 499202
11.5%
a 368640
 
8.5%
s 277200
 
6.4%
p 277200
 
6.4%
n 277200
 
6.4%
t 277200
 
6.4%
l 248996
 
5.7%
m 240958
 
5.6%
u 185760
 
4.3%
Other values (19) 1130822
26.1%

Monthly_Balance
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1762
Missing (%)1.2%
Memory size1.1 MiB

Credit_Score
Categorical

Distinct3
Distinct (%)< 0.1%
Missing50000
Missing (%)33.3%
Memory size1.1 MiB
Standard
53174 
Poor
28998 
Good
17828 

Length

Max length8
Median length8
Mean length6.12696
Min length4

Characters and Unicode

Total characters612696
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowGood
3rd rowGood
4th rowGood
5th rowGood

Common Values

ValueCountFrequency (%)
Standard 53174
35.4%
Poor 28998
19.3%
Good 17828
 
11.9%
(Missing) 50000
33.3%

Length

2023-04-06T09:10:04.501082image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-06T09:10:04.889139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
standard 53174
53.2%
poor 28998
29.0%
good 17828
 
17.8%

Most occurring characters

ValueCountFrequency (%)
d 124176
20.3%
a 106348
17.4%
o 93652
15.3%
r 82172
13.4%
S 53174
8.7%
t 53174
8.7%
n 53174
8.7%
P 28998
 
4.7%
G 17828
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 512696
83.7%
Uppercase Letter 100000
 
16.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 124176
24.2%
a 106348
20.7%
o 93652
18.3%
r 82172
16.0%
t 53174
10.4%
n 53174
10.4%
Uppercase Letter
ValueCountFrequency (%)
S 53174
53.2%
P 28998
29.0%
G 17828
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 612696
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 124176
20.3%
a 106348
17.4%
o 93652
15.3%
r 82172
13.4%
S 53174
8.7%
t 53174
8.7%
n 53174
8.7%
P 28998
 
4.7%
G 17828
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 612696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 124176
20.3%
a 106348
17.4%
o 93652
15.3%
r 82172
13.4%
S 53174
8.7%
t 53174
8.7%
n 53174
8.7%
P 28998
 
4.7%
G 17828
 
2.9%

Interactions

2023-04-06T09:09:50.721654image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:36.394431image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:38.453825image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:40.594213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:42.618590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:44.592082image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:46.626918image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:48.706881image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:50.965652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:36.662433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:38.827333image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:40.858180image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:42.878629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:44.856050image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:46.878886image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:48.961157image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:51.213614image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:36.930437image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:39.087829image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:41.114627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:43.126590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:45.114521image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:47.254883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:49.225614image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:51.453652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:37.204639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:39.343821image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:41.366633image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:43.374628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:45.374486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:47.498921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:49.481646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:51.712088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:37.465555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:39.599786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:41.618592image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:43.618588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:45.630484image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:47.746930image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:49.737654image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:51.952048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:37.725844image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:39.863786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:41.886591image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:43.878587image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:45.886924image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:47.994917image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:49.993613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:52.192052image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:37.973849image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:40.111785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:42.138591image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:44.122590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:46.142923image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:48.234886image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:50.249613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:52.426293image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:38.217805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:40.362221image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:42.390589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:44.365754image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:46.398884image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:48.474917image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-06T09:09:50.501612image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2023-04-06T09:10:05.031435image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Monthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateDelay_from_due_dateNum_Credit_InquiriesCredit_Utilization_RatioTotal_EMI_per_monthMonthOccupationCredit_MixPayment_of_Min_AmountPayment_BehaviourCredit_Score
Monthly_Inhand_Salary1.000-0.262-0.193-0.283-0.241-0.2610.1350.4510.0000.0270.2130.2300.1680.181
Num_Bank_Accounts-0.2621.0000.4000.5550.5570.482-0.0640.1060.0040.0000.0000.0040.0000.004
Num_Credit_Card-0.1930.4001.0000.4260.4220.390-0.0470.1000.0020.0040.0000.0000.0040.000
Interest_Rate-0.2830.5550.4261.0000.5490.569-0.0650.1380.0040.0040.0060.0040.0000.002
Delay_from_due_date-0.2410.5570.4220.5491.0000.490-0.0610.1320.0000.0260.4240.3570.0380.339
Num_Credit_Inquiries-0.2610.4820.3900.5690.4901.000-0.0660.1710.0030.0010.0020.0020.0040.008
Credit_Utilization_Ratio0.135-0.064-0.047-0.065-0.061-0.0661.0000.0080.0000.0030.0650.0740.0740.045
Total_EMI_per_month0.4510.1060.1000.1380.1320.1710.0081.0000.0040.0000.0000.0050.0010.005
Month0.0000.0040.0020.0040.0000.0030.0000.0041.0000.0000.0020.0000.0000.031
Occupation0.0270.0000.0040.0040.0260.0010.0030.0000.0001.0000.0230.0170.0030.027
Credit_Mix0.2130.0000.0000.0060.4240.0020.0650.0000.0020.0231.0000.4870.0620.402
Payment_of_Min_Amount0.2300.0040.0000.0040.3570.0020.0740.0050.0000.0170.4871.0000.0710.313
Payment_Behaviour0.1680.0000.0040.0000.0380.0040.0740.0010.0000.0030.0620.0711.0000.084
Credit_Score0.1810.0040.0000.0020.3390.0080.0450.0050.0310.0270.4020.3130.0841.000

Missing values

2023-04-06T09:09:53.148948image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-06T09:09:55.141223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-06T09:09:56.813275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDCustomer_IDMonthNameAgeSSNOccupationAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanType_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesCredit_MixOutstanding_DebtCredit_Utilization_RatioCredit_History_AgePayment_of_Min_AmountTotal_EMI_per_monthAmount_invested_monthlyPayment_BehaviourMonthly_BalanceCredit_Score
00x160aCUS_0xd40SeptemberAaron Maashoh23821-00-0265Scientist19114.121824.8433333434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan3711.272022.0Good809.9835.03040222 Years and 9 MonthsNo49.574949236.64268203272135Low_spent_Small_value_payments186.26670208571772NaN
10x160bCUS_0xd40OctoberAaron Maashoh24821-00-0265Scientist19114.121824.8433333434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan3913.274.0Good809.9833.05311422 Years and 10 MonthsNo49.57494921.465380264657146High_spent_Medium_value_payments361.44400385378196NaN
20x160cCUS_0xd40NovemberAaron Maashoh24821-00-0265Scientist19114.121824.8433333434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan-1412.274.0Good809.9833.811894NaNNo49.574949148.23393788500925Low_spent_Medium_value_payments264.67544623342997NaN
30x160dCUS_0xd40DecemberAaron Maashoh24_821-00-0265Scientist19114.12NaN3434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan4511.274.0Good809.9832.43055923 Years and 0 MonthsNo49.57494939.08251089460281High_spent_Medium_value_payments343.82687322383634NaN
40x1616CUS_0x21b1SeptemberRick Rothackerj28004-07-5839_______34847.843037.9866672461Credit-Builder Loan315.425.0Good605.0325.92682227 Years and 3 MonthsNo18.81621539.684018417945296High_spent_Large_value_payments485.2984336755923NaN
50x1617CUS_0x21b1OctoberRick Rothackerj28#F%$D@*&8Teacher34847.843037.9866672461Credit-Builder Loan335.425.0Good605.0330.11660027 Years and 4 MonthsNo18.816215251.62736875017606Low_spent_Large_value_payments303.3550833433617NaN
60x1618CUS_0x21b1NovemberRick Rothackerj28004-07-5839Teacher34847.843037.9866672461Credit-Builder Loan3NaN5.425.0_605.0330.99642427 Years and 5 MonthsNo18.81621572.68014533363515High_spent_Large_value_payments452.30230675990265NaN
70x1619CUS_0x21b1DecemberRick Rothackerj28004-07-5839Teacher34847.843037.9866672461Credit-Builder Loan32_7.425.0_605.0333.87516727 Years and 6 MonthsNo18.816215153.53448761392985!@9#%8421.44796447960783NaN
80x1622CUS_0x2dbcSeptemberLangep35486-85-3974Engineer143162.64NaN1583Auto Loan, Auto Loan, and Not Specified819427.13.0Good1303.0135.22970718 Years and 5 MonthsNo246.992319397.50365354404653Low_spent_Medium_value_payments854.2260270022115NaN
90x1623CUS_0x2dbcOctoberLangep35486-85-3974Engineer143162.6412187.2200001583Auto Loan, Auto Loan, and Not Specified632.13.0Good1303.0135.68583618 Years and 6 MonthsNo246.992319453.6151305781054Low_spent_Large_value_payments788.1145499681528NaN
IDCustomer_IDMonthNameAgeSSNOccupationAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanType_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesCredit_MixOutstanding_DebtCredit_Utilization_RatioCredit_History_AgePayment_of_Min_AmountTotal_EMI_per_monthAmount_invested_monthlyPayment_BehaviourMonthly_BalanceCredit_Score
1499900x25fe0CUS_0x8600JulySarah McBridec28031-35-0942Architect20002.881929.906667108295Personal Loan, Auto Loan, Mortgage Loan, Student Loan, and Student Loan332618.319.0Bad3571.725.123535NaNYes60.964772173.2755025599617Low_spent_Large_value_payments228.750392Standard
1499910x25fe1CUS_0x8600AugustSarah McBridec29031-35-0942Architect20002.881929.906667108295Personal Loan, Auto Loan, Mortgage Loan, Student Loan, and Student Loan332518.319.0Bad3571.737.1407846 Years and 3 MonthsYes60.96477234.66290609052614High_spent_Large_value_payments337.362988Standard
1499920x25fe6CUS_0x942cJanuaryNicks24078-73-5990Mechanic39628.993359.4158334672Auto Loan, and Student Loan23NaN9.53.0_502.3832.99133331 Years and 3 MonthsNo35.104023401.1964806036356Low_spent_Small_value_payments189.64108Poor
1499930x25fe7CUS_0x942cFebruaryNicks25078-73-5990Mechanic39628.99_3359.4158334672Auto Loan, and Student Loan23NaN11.53.0Good502.3829.13544731 Years and 4 MonthsNo58638.000000180.7330951944497Low_spent_Medium_value_payments400.104466Standard
1499940x25fe8CUS_0x942cMarchNicks25078-73-5990Mechanic39628.993359.4158334672Auto Loan, and Student Loan2069.53.0_502.3839.32356931 Years and 5 MonthsNo35.104023140.58140274528395High_spent_Medium_value_payments410.256158Poor
1499950x25fe9CUS_0x942cAprilNicks25078-73-5990Mechanic39628.993359.4158334672Auto Loan, and Student Loan23711.53.0_502.3834.66357231 Years and 6 MonthsNo35.10402360.97133255718485High_spent_Large_value_payments479.866228Poor
1499960x25feaCUS_0x942cMayNicks25078-73-5990Mechanic39628.993359.4158334672Auto Loan, and Student Loan18711.53.0_502.3840.56563131 Years and 7 MonthsNo35.10402354.18595028760385High_spent_Medium_value_payments496.65161Poor
1499970x25febCUS_0x942cJuneNicks25078-73-5990Mechanic39628.993359.4158334657292Auto Loan, and Student Loan27611.53.0Good502.3841.25552231 Years and 8 MonthsNo35.10402324.02847744864441High_spent_Large_value_payments516.809083Poor
1499980x25fecCUS_0x942cJulyNicks25078-73-5990Mechanic39628.993359.4158334672Auto Loan, and Student Loan20NaN11.53.0Good502.3833.63820831 Years and 9 MonthsNo35.104023251.67258219721603Low_spent_Large_value_payments319.164979Standard
1499990x25fedCUS_0x942cAugustNicks25078-73-5990Mechanic39628.99_3359.4158334672Auto Loan, and Student Loan18611.53.0Good502.3834.19246331 Years and 10 MonthsNo35.104023167.1638651610451!@9#%8393.673696Poor